Type of Document	Dissertation
Author	Jang, Kun-Il
Author's Email Address	kijang@met.fsu.edu
URN	etd-03292004-162608
Title	4D-Var Assimilation of Toms Ozone Measurements for the Prediction of Mid-Latitude Winter Storms
Degree	Doctor of Philosophy
Department	Meteorology, Department of
Advisory Committee	Advisor Name Title Xialoei Zou Committee Chair
Keywords	Winter Storm TOMS Ozone 4D-Var
Date of Defense	2004-03-23
Availability	unrestricted
Abstract In this study, two kinds of methodology are proposed for incorporating total column zone data from the Total Ozone Mapping Spectrometer (TOMS) into initial conditions of a mesoscale prediction model. The first methodology is based on the strong linear correlation between vertical mean potential vorticity (MPV) and TOMS ozone (O3). The second methodology assimilates the TOMS ozone observations directly by adding the ozone transport equation into the MM5 model and its adjoint. The three-dimensional ozone initial condition for the transport equation is estimated from the observed ozone. The proposed first approach of ozone assimilation is applied to two case studies. The first case is the notable Washington D. C. snow storm (to be called DCSTORM) that occurred between 24 - 25 January 2000 along the East Coast of the United States. The second case is an Atlantic Ocean winter storm that was observed between 14-16 February 1997 (to be called AOSTORM). It is found that adjustments in model initial conditions assimilating TOMS ozone-only data are confined to the upper levels and produced almost no impact to the prediction of the storm development. However, when TOMS ozone data are used together with radiosonde observations, a more rapid deepening of the sea level pressure of the simulated storm is observed than with radiosonde-only observations. The predicted motion of the DCSTORM is also altered, with a track closer to the coast. On the contrary, assimilation of only TOMS ozone data produces non-negligible adjustment of wind and temperature fields at all levels in the AOSTORM case. When compared with dropsonde observations, TOMS ozone data improves model forecasts of both temperature and moisture fields. Adjoint sensitivity studies indicate that the significant impact of TOMS ozone on cyclone prediction is expected if large TOMS ozone anomalies appear in a region where model error is grow.
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